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The recent trend in data mining is integrating multi-modal and multi-dimensional data for improved reservoir properties prediction.
Also, data mining techniques is capable of handling large dimensional data whereas statistical techniques have some limitations [3, 13].
My main research focus currently is on visualization techniques for interactively exploring large multi-dimensional data warehouses within the Rivet computer systems visualization project with Robert Bosch and Chris Stolte.
He is also working on forecasting techniques and data clustering of multi-dimensional data.
Principal Component Analysis (PCA) is a multivariate data analysis technique which reduces the multi-dimensional data into two principal components.
This paper presents a novel visual exploration approach for mining abstract, multi-dimensional data stored in tables in a relational database.
There is a strong need to review and integrate multi-dimensional data for follow up validation.
Data mining allows for a multi-dimensional analysis facilitating sets of relevant attributes of subjects (e.g. employees, user accounts, or entitlements) and objects (e.g. amount, frequency, or criticality of data accessed).
The CSEOF technique is an excellent tool for analyzing variability in space time or multi-dimensional data.
Koudas et al. [12] introduce DISC, a technique for continuous monitoring approximate k-NN queries over multi-dimensional data streams.
We identify the important factors for an efficient multi-disk searching of multi-dimensional data and develop secondary storage organization and retrieval techniques that directly address these factors.
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